{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e8faa69b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-06T04:20:36.287735Z",
     "start_time": "2022-03-06T04:20:36.282042Z"
    }
   },
   "outputs": [],
   "source": [
    "# !pip install spinesTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "495fb674",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-06T04:20:38.129111Z",
     "start_time": "2022-03-06T04:20:36.288810Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from spinesTS.nn import DenseNet1DTorchModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b33fc647",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-06T04:20:38.133062Z",
     "start_time": "2022-03-06T04:20:38.130143Z"
    }
   },
   "outputs": [],
   "source": [
    "from spinesTS.preprocessing import split_series\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from spinesTS.plotting import plot2d\n",
    "from sklearn.metrics import r2_score\n",
    "from spinesTS.pipeline import Pipeline\n",
    "\n",
    "from spinesTS.data import BuiltInSeriesData\n",
    "\n",
    "from spinesTS.metrics import mean_absolute_error\n",
    "from spinesTS.metrics import mean_absolute_percentage_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ff73232c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-06T04:20:38.180029Z",
     "start_time": "2022-03-06T04:20:38.134336Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Existing CSV file list: \n",
      ">> >> >> >> >> >> >> >> >> >> \n",
      "    Electric_Production\n",
      "    Messages_Sent\n",
      "    Messages_Sent_Hour\n",
      "    Series_0\n",
      "    Series_1\n",
      "    Series_2\n",
      "    Series_3\n",
      "    Series_4\n",
      "    Series_5\n",
      "    Series_6\n",
      "    Series_7\n",
      "    Series_8\n",
      "    Series_9\n",
      "    Supermarket_Incoming\n",
      "    Web_Sales\n",
      "<< << << << << << << << << << \n"
     ]
    }
   ],
   "source": [
    "series_data = BuiltInSeriesData()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "34432695",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-06T04:20:39.035316Z",
     "start_time": "2022-03-06T04:20:38.181001Z"
    }
   },
   "outputs": [],
   "source": [
    "cs = series_data['Series_0']\n",
    "cs_data = cs.dataset['turnover_1']\n",
    "x_train_cs, x_test_cs, y_train_cs, y_test_cs = split_series(cs_data, cs_data, 420, 420, train_size=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "32edf157",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-06T04:20:39.070856Z",
     "start_time": "2022-03-06T04:20:39.036479Z"
    }
   },
   "outputs": [],
   "source": [
    "multi_reg = DenseNet1DTorchModel(420, random_seed=666, learning_rate=0.001, kernel_size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ed9465a7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-06T04:20:41.489912Z",
     "start_time": "2022-03-06T04:20:39.071953Z"
    },
    "scrolled": false,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000 \n",
      "282/282 - p0 - loss: 114.6241 - mae: 78.2985 - val_loss: 82.5812 - val_mae: 83.0796 - 4.71s/epoch - 0.017s/step\n",
      "Epoch 2/1000 \n",
      "282/282 - p1 - loss: 103.0269 - mae: 62.8971 - val_loss: 111.7349 - val_mae: 112.2341 - 4.45s/epoch - 0.016s/step\n",
      "Epoch 3/1000 \n",
      "282/282 - p2 - loss: 101.7815 - mae: 59.1459 - val_loss: 93.1271 - val_mae: 93.6261 - 4.64s/epoch - 0.016s/step\n",
      "Epoch 4/1000 \n",
      "282/282 - p3 - loss: 100.1614 - mae: 57.0847 - val_loss: 91.7971 - val_mae: 92.2960 - 4.80s/epoch - 0.017s/step\n",
      "Epoch 5/1000 \n",
      "282/282 - p4 - loss: 97.4103 - mae: 55.1768 - val_loss: 85.8342 - val_mae: 86.3329 - 4.54s/epoch - 0.016s/step\n",
      "Epoch 6/1000 \n",
      "282/282 - p5 - loss: 94.9194 - mae: 52.2879 - val_loss: 84.5983 - val_mae: 85.0970 - 4.66s/epoch - 0.017s/step\n",
      "Epoch 7/1000 \n",
      "282/282 - p6 - loss: 91.9427 - mae: 47.6715 - val_loss: 83.3044 - val_mae: 83.8030 - 4.48s/epoch - 0.016s/step\n",
      "Epoch 8/1000 \n",
      "282/282 - p0 - loss: 90.8595 - mae: 45.9251 - val_loss: 79.4921 - val_mae: 79.9906 - 4.67s/epoch - 0.017s/step\n",
      "Epoch 9/1000 \n",
      "282/282 - p1 - loss: 90.0918 - mae: 46.2324 - val_loss: 80.1744 - val_mae: 80.6730 - 4.56s/epoch - 0.016s/step\n",
      "Epoch 10/1000 \n",
      "282/282 - p2 - loss: 90.5317 - mae: 45.6909 - val_loss: 81.8178 - val_mae: 82.3165 - 4.46s/epoch - 0.016s/step\n",
      "Epoch 11/1000 \n",
      "282/282 - p0 - loss: 89.3752 - mae: 46.1805 - val_loss: 76.1560 - val_mae: 76.6544 - 4.26s/epoch - 0.015s/step\n",
      "Epoch 12/1000 \n",
      "282/282 - p1 - loss: 88.1295 - mae: 45.1267 - val_loss: 77.7023 - val_mae: 78.2008 - 4.26s/epoch - 0.015s/step\n",
      "Epoch 13/1000 \n",
      "282/282 - p2 - loss: 88.6391 - mae: 44.6482 - val_loss: 78.3564 - val_mae: 78.8549 - 4.53s/epoch - 0.016s/step\n",
      "Epoch 14/1000 \n",
      "282/282 - p3 - loss: 87.5862 - mae: 43.8843 - val_loss: 82.8851 - val_mae: 83.3837 - 4.50s/epoch - 0.016s/step\n",
      "Epoch 15/1000 \n",
      "282/282 - p4 - loss: 86.9459 - mae: 43.5681 - val_loss: 87.9605 - val_mae: 88.4592 - 4.37s/epoch - 0.016s/step\n",
      "Epoch 16/1000 \n",
      "282/282 - p5 - loss: 86.0034 - mae: 42.9853 - val_loss: 87.1162 - val_mae: 87.6149 - 4.65s/epoch - 0.016s/step\n",
      "Epoch 17/1000 \n",
      "282/282 - p6 - loss: 85.9539 - mae: 42.5852 - val_loss: 86.5785 - val_mae: 87.0772 - 4.68s/epoch - 0.017s/step\n",
      "Epoch 18/1000 \n",
      "282/282 - p7 - loss: 85.2045 - mae: 42.0610 - val_loss: 87.5160 - val_mae: 88.0148 - 4.52s/epoch - 0.016s/step\n",
      "Epoch 19/1000 \n",
      "282/282 - p8 - loss: 85.0531 - mae: 41.5897 - val_loss: 91.0638 - val_mae: 91.5626 - 4.37s/epoch - 0.015s/step\n",
      "Epoch 20/1000 \n",
      "282/282 - p9 - loss: 84.9395 - mae: 41.6424 - val_loss: 88.5240 - val_mae: 89.0228 - 4.44s/epoch - 0.016s/step\n",
      "Epoch 21/1000 \n",
      "282/282 - p10 - loss: 84.7225 - mae: 42.8820 - val_loss: 86.1483 - val_mae: 86.6470 - 4.46s/epoch - 0.016s/step\n",
      "Epoch 22/1000 \n",
      "282/282 - p11 - loss: 84.8395 - mae: 42.9394 - val_loss: 84.2486 - val_mae: 84.7472 - 4.56s/epoch - 0.016s/step\n",
      "Epoch 23/1000 \n",
      "282/282 - p12 - loss: 84.4228 - mae: 42.8875 - val_loss: 87.5459 - val_mae: 88.0447 - 4.59s/epoch - 0.016s/step\n",
      "Epoch 24/1000 \n",
      "282/282 - p13 - loss: 83.9811 - mae: 42.9197 - val_loss: 87.2016 - val_mae: 87.7003 - 4.71s/epoch - 0.017s/step\n",
      "Epoch 25/1000 \n",
      "282/282 - p14 - loss: 83.4945 - mae: 42.3116 - val_loss: 86.0641 - val_mae: 86.5628 - 5.13s/epoch - 0.018s/step\n",
      "Epoch 26/1000 \n",
      "282/282 - p15 - loss: 83.8904 - mae: 42.0329 - val_loss: 84.8282 - val_mae: 85.3268 - 4.92s/epoch - 0.017s/step\n",
      "Epoch 27/1000 \n",
      "282/282 - p16 - loss: 83.9522 - mae: 41.7843 - val_loss: 86.6157 - val_mae: 87.1145 - 4.90s/epoch - 0.017s/step\n",
      "Epoch 28/1000 \n",
      "282/282 - p17 - loss: 83.8164 - mae: 41.4461 - val_loss: 88.9751 - val_mae: 89.4738 - 4.94s/epoch - 0.018s/step\n",
      "Epoch 29/1000 \n",
      "282/282 - p18 - loss: 83.7280 - mae: 41.4847 - val_loss: 87.2097 - val_mae: 87.7085 - 4.99s/epoch - 0.018s/step\n",
      "Epoch 30/1000 \n",
      "282/282 - p19 - loss: 83.1937 - mae: 41.6490 - val_loss: 85.7191 - val_mae: 86.2178 - 4.95s/epoch - 0.018s/step\n",
      "Epoch 31/1000 \n",
      "282/282 - p20 - loss: 83.0857 - mae: 41.2277 - val_loss: 82.6331 - val_mae: 83.1318 - 4.91s/epoch - 0.017s/step\n",
      "Epoch 32/1000 \n",
      "282/282 - p21 - loss: 83.1205 - mae: 40.5172 - val_loss: 83.2276 - val_mae: 83.7262 - 4.91s/epoch - 0.017s/step\n",
      "Epoch 33/1000 \n",
      "282/282 - p22 - loss: 82.9701 - mae: 40.1839 - val_loss: 86.6936 - val_mae: 87.1923 - 4.97s/epoch - 0.018s/step\n",
      "Epoch 34/1000 \n",
      "282/282 - p23 - loss: 82.8757 - mae: 39.7158 - val_loss: 89.4513 - val_mae: 89.9501 - 4.93s/epoch - 0.017s/step\n",
      "Epoch 35/1000 \n",
      "282/282 - p24 - loss: 82.3428 - mae: 39.3294 - val_loss: 89.0028 - val_mae: 89.5017 - 4.88s/epoch - 0.017s/step\n",
      "Epoch 36/1000 \n",
      "282/282 - p25 - loss: 81.9999 - mae: 39.1188 - val_loss: 85.9486 - val_mae: 86.4473 - 4.89s/epoch - 0.017s/step\n",
      "Epoch 37/1000 \n",
      "282/282 - p26 - loss: 81.8764 - mae: 39.0025 - val_loss: 85.7710 - val_mae: 86.2698 - 4.93s/epoch - 0.017s/step\n",
      "Epoch 38/1000 \n",
      "282/282 - p27 - loss: 81.6313 - mae: 38.6225 - val_loss: 88.0921 - val_mae: 88.5910 - 4.88s/epoch - 0.017s/step\n",
      "Epoch 39/1000 \n",
      "282/282 - p28 - loss: 81.5089 - mae: 38.3569 - val_loss: 89.0401 - val_mae: 89.5390 - 4.86s/epoch - 0.017s/step\n",
      "Epoch 40/1000 \n",
      "282/282 - p29 - loss: 81.1096 - mae: 38.1606 - val_loss: 88.0709 - val_mae: 88.5698 - 5.18s/epoch - 0.018s/step\n",
      "Epoch 41/1000 \n",
      "282/282 - p30 - loss: 80.7884 - mae: 38.0201 - val_loss: 87.7243 - val_mae: 88.2232 - 5.42s/epoch - 0.019s/step\n",
      "Epoch 42/1000 \n",
      "282/282 - p31 - loss: 80.6788 - mae: 37.6051 - val_loss: 87.8544 - val_mae: 88.3533 - 5.20s/epoch - 0.018s/step\n",
      "Epoch 43/1000 \n",
      "282/282 - p32 - loss: 80.6149 - mae: 37.2696 - val_loss: 85.9378 - val_mae: 86.4367 - 4.90s/epoch - 0.017s/step\n",
      "Epoch 44/1000 \n",
      "282/282 - p33 - loss: 80.2822 - mae: 37.1226 - val_loss: 84.8463 - val_mae: 85.3451 - 4.93s/epoch - 0.017s/step\n",
      "Epoch 45/1000 \n",
      "282/282 - p34 - loss: 79.9976 - mae: 37.0701 - val_loss: 87.0252 - val_mae: 87.5241 - 5.16s/epoch - 0.018s/step\n",
      "Epoch 46/1000 \n",
      "282/282 - p35 - loss: 80.0859 - mae: 37.1750 - val_loss: 90.2413 - val_mae: 90.7403 - 4.92s/epoch - 0.017s/step\n",
      "Epoch 47/1000 \n",
      "282/282 - p36 - loss: 79.9213 - mae: 36.6989 - val_loss: 91.6589 - val_mae: 92.1580 - 4.92s/epoch - 0.017s/step\n",
      "Epoch 48/1000 \n",
      "282/282 - p37 - loss: 79.7353 - mae: 36.3498 - val_loss: 93.3306 - val_mae: 93.8297 - 5.23s/epoch - 0.019s/step\n",
      "Epoch 49/1000 \n",
      "282/282 - p38 - loss: 79.7029 - mae: 35.9939 - val_loss: 93.7645 - val_mae: 94.2636 - 4.90s/epoch - 0.017s/step\n",
      "Epoch 50/1000 \n",
      "282/282 - p39 - loss: 79.0746 - mae: 35.8508 - val_loss: 92.6366 - val_mae: 93.1356 - 5.08s/epoch - 0.018s/step\n",
      "Epoch 51/1000 \n",
      "282/282 - p40 - loss: 79.3296 - mae: 36.0162 - val_loss: 88.6427 - val_mae: 89.1417 - 5.22s/epoch - 0.019s/step\n",
      "Epoch 52/1000 \n",
      "282/282 - p41 - loss: 79.2751 - mae: 35.1975 - val_loss: 84.9623 - val_mae: 85.4612 - 4.94s/epoch - 0.018s/step\n",
      "Epoch 53/1000 \n",
      "282/282 - p42 - loss: 78.8384 - mae: 34.9975 - val_loss: 86.8063 - val_mae: 87.3053 - 4.96s/epoch - 0.018s/step\n",
      "Epoch 54/1000 \n",
      "282/282 - p43 - loss: 78.7582 - mae: 34.8474 - val_loss: 85.9725 - val_mae: 86.4714 - 4.94s/epoch - 0.018s/step\n",
      "Epoch 55/1000 \n",
      "282/282 - p44 - loss: 79.1134 - mae: 34.7778 - val_loss: 86.0235 - val_mae: 86.5224 - 4.89s/epoch - 0.017s/step\n",
      "Epoch 56/1000 \n",
      "282/282 - p45 - loss: 78.9534 - mae: 35.0707 - val_loss: 87.2524 - val_mae: 87.7514 - 4.93s/epoch - 0.017s/step\n",
      "Epoch 57/1000 \n",
      "282/282 - p46 - loss: 78.3040 - mae: 34.7141 - val_loss: 91.3873 - val_mae: 91.8864 - 4.90s/epoch - 0.017s/step\n",
      "Epoch 58/1000 \n",
      "282/282 - p47 - loss: 77.9735 - mae: 34.6673 - val_loss: 91.7247 - val_mae: 92.2238 - 4.90s/epoch - 0.017s/step\n",
      "Epoch 59/1000 \n",
      "282/282 - p48 - loss: 78.8009 - mae: 34.4975 - val_loss: 87.3722 - val_mae: 87.8713 - 4.92s/epoch - 0.017s/step\n",
      "Epoch 60/1000 \n",
      "282/282 - p49 - loss: 78.1902 - mae: 34.2516 - val_loss: 87.9683 - val_mae: 88.4674 - 4.91s/epoch - 0.017s/step\n",
      "Epoch 61/1000 \n",
      "282/282 - p50 - loss: 78.3281 - mae: 34.0154 - val_loss: 86.8692 - val_mae: 87.3682 - 4.92s/epoch - 0.017s/step\n",
      "Epoch 62/1000 \n",
      "282/282 - p51 - loss: 78.2655 - mae: 33.7853 - val_loss: 84.2593 - val_mae: 84.7583 - 4.96s/epoch - 0.018s/step\n",
      "Epoch 63/1000 \n",
      "282/282 - p52 - loss: 78.5542 - mae: 33.7412 - val_loss: 86.8193 - val_mae: 87.3184 - 4.90s/epoch - 0.017s/step\n",
      "Epoch 64/1000 \n",
      "282/282 - p53 - loss: 78.2735 - mae: 33.5677 - val_loss: 89.9057 - val_mae: 90.4047 - 4.94s/epoch - 0.018s/step\n",
      "Epoch 65/1000 \n",
      "282/282 - p54 - loss: 77.9627 - mae: 33.4042 - val_loss: 92.9973 - val_mae: 93.4964 - 4.86s/epoch - 0.017s/step\n",
      "Epoch 66/1000 \n",
      "282/282 - p55 - loss: 77.7470 - mae: 33.2311 - val_loss: 89.7048 - val_mae: 90.2038 - 4.85s/epoch - 0.017s/step\n",
      "Epoch 67/1000 \n",
      "282/282 - p56 - loss: 77.4448 - mae: 33.0444 - val_loss: 89.2415 - val_mae: 89.7405 - 4.84s/epoch - 0.017s/step\n",
      "Epoch 68/1000 \n",
      "282/282 - p57 - loss: 77.3455 - mae: 32.9891 - val_loss: 86.4137 - val_mae: 86.9127 - 5.31s/epoch - 0.019s/step\n",
      "Epoch 69/1000 \n",
      "282/282 - p58 - loss: 77.4854 - mae: 32.9339 - val_loss: 87.5185 - val_mae: 88.0175 - 4.91s/epoch - 0.017s/step\n",
      "Epoch 70/1000 \n",
      "282/282 - p59 - loss: 77.5555 - mae: 32.8608 - val_loss: 88.1262 - val_mae: 88.6253 - 4.86s/epoch - 0.017s/step\n",
      "Epoch 71/1000 \n",
      "282/282 - p60 - loss: 77.2297 - mae: 32.8211 - val_loss: 87.1580 - val_mae: 87.6570 - 4.91s/epoch - 0.017s/step\n",
      "Epoch 72/1000 \n",
      "282/282 - p61 - loss: 76.6114 - mae: 32.6708 - val_loss: 89.3238 - val_mae: 89.8229 - 4.89s/epoch - 0.017s/step\n",
      "Epoch 73/1000 \n",
      "282/282 - p62 - loss: 76.6356 - mae: 32.4281 - val_loss: 84.8732 - val_mae: 85.3722 - 4.90s/epoch - 0.017s/step\n",
      "Epoch 74/1000 \n",
      "282/282 - p63 - loss: 77.0114 - mae: 32.5266 - val_loss: 84.2627 - val_mae: 84.7616 - 4.92s/epoch - 0.017s/step\n",
      "Epoch 75/1000 \n",
      "282/282 - p64 - loss: 76.5359 - mae: 32.3336 - val_loss: 85.3386 - val_mae: 85.8376 - 4.87s/epoch - 0.017s/step\n",
      "Epoch 76/1000 \n",
      "282/282 - p65 - loss: 76.5407 - mae: 32.2651 - val_loss: 87.5326 - val_mae: 88.0316 - 4.82s/epoch - 0.017s/step\n",
      "Epoch 77/1000 \n",
      "282/282 - p66 - loss: 76.0281 - mae: 32.1677 - val_loss: 88.7670 - val_mae: 89.2661 - 4.89s/epoch - 0.017s/step\n",
      "Epoch 78/1000 \n",
      "282/282 - p67 - loss: 76.3079 - mae: 32.0368 - val_loss: 90.0897 - val_mae: 90.5888 - 5.09s/epoch - 0.018s/step\n",
      "Epoch 79/1000 \n",
      "282/282 - p68 - loss: 76.0860 - mae: 32.0045 - val_loss: 88.8919 - val_mae: 89.3910 - 4.89s/epoch - 0.017s/step\n",
      "Epoch 80/1000 \n",
      "282/282 - p69 - loss: 76.1220 - mae: 31.9741 - val_loss: 87.7366 - val_mae: 88.2356 - 4.86s/epoch - 0.017s/step\n",
      "Epoch 81/1000 \n",
      "282/282 - p70 - loss: 75.9421 - mae: 31.8542 - val_loss: 90.7350 - val_mae: 91.2341 - 4.87s/epoch - 0.017s/step\n",
      "Epoch 82/1000 \n",
      "282/282 - p71 - loss: 75.8751 - mae: 31.7322 - val_loss: 90.6205 - val_mae: 91.1196 - 4.90s/epoch - 0.017s/step\n",
      "Epoch 83/1000 \n",
      "282/282 - p72 - loss: 75.3940 - mae: 31.5383 - val_loss: 87.3758 - val_mae: 87.8747 - 4.92s/epoch - 0.017s/step\n",
      "Epoch 84/1000 \n",
      "282/282 - p73 - loss: 75.8524 - mae: 31.5416 - val_loss: 86.5496 - val_mae: 87.0484 - 5.06s/epoch - 0.018s/step\n",
      "Epoch 85/1000 \n",
      "282/282 - p74 - loss: 75.1896 - mae: 31.4707 - val_loss: 87.8636 - val_mae: 88.3625 - 5.00s/epoch - 0.018s/step\n",
      "Epoch 86/1000 \n",
      "282/282 - p75 - loss: 75.4293 - mae: 31.5116 - val_loss: 87.0387 - val_mae: 87.5377 - 4.96s/epoch - 0.018s/step\n",
      "Epoch 87/1000 \n",
      "282/282 - p76 - loss: 74.8908 - mae: 31.3275 - val_loss: 87.6719 - val_mae: 88.1710 - 4.95s/epoch - 0.018s/step\n",
      "Epoch 88/1000 \n",
      "282/282 - p77 - loss: 74.6632 - mae: 31.2074 - val_loss: 86.8046 - val_mae: 87.3037 - 4.91s/epoch - 0.017s/step\n",
      "Epoch 89/1000 \n",
      "282/282 - p78 - loss: 75.0886 - mae: 31.1508 - val_loss: 86.6335 - val_mae: 87.1326 - 4.92s/epoch - 0.017s/step\n",
      "Epoch 90/1000 \n",
      "282/282 - p79 - loss: 74.1486 - mae: 31.0557 - val_loss: 87.6727 - val_mae: 88.1717 - 4.97s/epoch - 0.018s/step\n",
      "Epoch 91/1000 \n",
      "282/282 - p80 - loss: 74.2596 - mae: 30.9661 - val_loss: 84.9606 - val_mae: 85.4595 - 4.95s/epoch - 0.018s/step\n",
      "Epoch 92/1000 \n",
      "282/282 - p81 - loss: 74.1107 - mae: 30.8694 - val_loss: 82.2075 - val_mae: 82.7063 - 4.94s/epoch - 0.018s/step\n",
      "Epoch 93/1000 \n",
      "282/282 - p82 - loss: 74.6756 - mae: 30.7863 - val_loss: 86.0512 - val_mae: 86.5502 - 5.29s/epoch - 0.019s/step\n",
      "Epoch 94/1000 \n",
      "282/282 - p83 - loss: 74.1958 - mae: 30.6872 - val_loss: 88.0385 - val_mae: 88.5375 - 4.98s/epoch - 0.018s/step\n",
      "Epoch 95/1000 \n",
      "282/282 - p84 - loss: 74.7070 - mae: 30.6549 - val_loss: 87.4478 - val_mae: 87.9468 - 4.91s/epoch - 0.017s/step\n",
      "Epoch 96/1000 \n",
      "282/282 - p85 - loss: 73.8894 - mae: 30.5602 - val_loss: 83.7039 - val_mae: 84.2028 - 5.04s/epoch - 0.018s/step\n",
      "Epoch 97/1000 \n",
      "282/282 - p86 - loss: 74.1817 - mae: 30.4413 - val_loss: 83.2604 - val_mae: 83.7593 - 4.90s/epoch - 0.017s/step\n",
      "Epoch 98/1000 \n",
      "282/282 - p87 - loss: 74.3706 - mae: 30.3383 - val_loss: 83.9160 - val_mae: 84.4150 - 4.89s/epoch - 0.017s/step\n",
      "Epoch 99/1000 \n",
      "282/282 - p88 - loss: 74.5114 - mae: 30.2469 - val_loss: 85.4300 - val_mae: 85.9289 - 4.97s/epoch - 0.018s/step\n",
      "Epoch 100/1000 \n",
      "282/282 - p89 - loss: 74.6027 - mae: 30.2114 - val_loss: 90.3985 - val_mae: 90.8975 - 4.96s/epoch - 0.018s/step\n",
      "Epoch 101/1000 \n",
      "282/282 - p90 - loss: 73.4705 - mae: 30.0530 - val_loss: 89.2881 - val_mae: 89.7871 - 5.03s/epoch - 0.018s/step\n",
      "Epoch 102/1000 \n",
      "282/282 - p91 - loss: 73.1176 - mae: 30.0462 - val_loss: 84.3172 - val_mae: 84.8162 - 4.98s/epoch - 0.018s/step\n",
      "Epoch 103/1000 \n",
      "282/282 - p92 - loss: 74.3452 - mae: 29.9151 - val_loss: 83.1097 - val_mae: 83.6086 - 4.94s/epoch - 0.018s/step\n",
      "Epoch 104/1000 \n",
      "282/282 - p93 - loss: 73.2038 - mae: 29.8932 - val_loss: 83.8789 - val_mae: 84.3778 - 4.97s/epoch - 0.018s/step\n",
      "Epoch 105/1000 \n",
      "282/282 - p94 - loss: 73.3327 - mae: 29.7037 - val_loss: 83.3062 - val_mae: 83.8051 - 4.88s/epoch - 0.017s/step\n",
      "Epoch 106/1000 \n",
      "282/282 - p95 - loss: 73.0855 - mae: 29.5919 - val_loss: 83.9860 - val_mae: 84.4850 - 4.89s/epoch - 0.017s/step\n",
      "Epoch 107/1000 \n",
      "282/282 - p96 - loss: 72.8150 - mae: 29.6187 - val_loss: 84.4009 - val_mae: 84.8999 - 4.90s/epoch - 0.017s/step\n",
      "Epoch 108/1000 \n",
      "282/282 - p97 - loss: 73.2606 - mae: 29.6752 - val_loss: 85.4671 - val_mae: 85.9660 - 4.88s/epoch - 0.017s/step\n",
      "Epoch 109/1000 \n",
      "282/282 - p98 - loss: 73.2257 - mae: 29.5544 - val_loss: 88.9677 - val_mae: 89.4668 - 4.85s/epoch - 0.017s/step\n",
      "Epoch 110/1000 \n",
      "282/282 - p99 - loss: 72.2624 - mae: 29.4784 - val_loss: 88.6480 - val_mae: 89.1471 - 4.88s/epoch - 0.017s/step\n",
      "Epoch 111/1000 \n",
      "282/282 - p100 - loss: 71.9819 - mae: 29.4097 - val_loss: 85.8871 - val_mae: 86.3862 - 4.86s/epoch - 0.017s/step\n",
      "r2: -0.02840672110943554\n",
      "mae: 75.62427333650135\n",
      "mape: 0.7064236968498963\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "multi_reg.fit(x_train_cs, y_train_cs, eval_set=(x_test_cs[:-2], y_test_cs[:-2]), batch_size=32,\n",
    "             min_delta=0, patience=100, epochs=1000, verbose=True)\n",
    "y_pred_cs = multi_reg.predict(x_test_cs[-2:])\n",
    "print(f\"r2: {r2_score(y_test_cs[-2:].T, y_pred_cs.T)}\")\n",
    "print(f\"mae: {mean_absolute_error(y_test_cs[-2:], y_pred_cs)}\")\n",
    "print(f\"mape: {mean_absolute_percentage_error(y_test_cs[-2:], y_pred_cs)}\")\n",
    "plot2d(y_test_cs[-2:], y_pred_cs, fig_num_or_slice=slice(-1, None), labels=['y_test', 'y_pred'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62636718",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4cba32d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22badd39",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a3d9d5b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9024fa36",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
